System, method, and computer program product for detecting road marking points from LiDAR data

3. The computer implemented method of claim 1, wherein determining whether the at least one candidate pair of gradient edge points corresponds to a road marking edge is based on at least one of the following parameters: an angular arc of the interior segment of points, a Euclidean distance of the interior segment of points, a maximum distance between consecutive points in the interior segment of points, a minimum intensity of the points in the interior segment of points, a ratio of the minimum intensity of the points in the interior segment of points to a maximum intensity of the points in the interior segment of points, a difference between a mean interior intensity of the interior segment of points and a mean exterior intensity of the exterior segments of points, a standard deviation of the interior segment of points, or any combination thereof.

4. The computer implemented method of claim 1, wherein point clouds of the plurality of point clouds associated with the plurality of laser emitters are processed in parallel using multithreading.

5. The computer implemented method of claim 1, wherein each LiDAR point cloud of the plurality of point clouds corresponds to a single 360 degree sweep of the LiDAR system.

9. The system of claim 7, wherein determining whether the at least one candidate pair of gradient edge points corresponds to a road marking edge is based on at least one of the following parameters: an angular arc of the interior segment of points, a Euclidean distance of the interior segment of points, a maximum distance between consecutive points in the interior segment of points, a minimum intensity of the points in the interior segment of points, a ratio of the minimum intensity of the points in the interior segment of points to a maximum intensity of the points in the interior segment of points, a difference between a mean interior intensity of the interior segment of points and a mean exterior intensity of the exterior segments of points, a standard deviation of the interior segment of points, or any combination thereof.

10. The system of claim 7, wherein the one or more processors are further programmed and/or configured to process point clouds of the plurality of point clouds associated with the plurality of laser emitters in parallel using multithreading.

11. The system of claim 7, wherein each LiDAR point cloud of the plurality of point clouds corresponds to a single 360 degree sweep of the LiDAR system.

15. The autonomous vehicle of claim 13, wherein determining whether the at least one candidate pair of gradient edge points corresponds to a road marking edge is based on at least one of the following parameters: an angular arc of the interior segment of points, a Euclidean distance of the interior segment of points, a maximum distance between consecutive points in the interior segment of points, a minimum intensity of the points in the interior segment of points, a ratio of the minimum intensity of the points in the interior segment of points to a maximum intensity of the points in the interior segment of points, a difference between a mean interior intensity of the interior segment of points and a mean exterior intensity of the exterior segments of points, a standard deviation of the interior segment of points, or any combination thereof.

16. The autonomous vehicle of claim 13, wherein the computing device is further programmed and/or configured to process point clouds of the plurality of point clouds associated with the plurality of laser emitters in parallel using multithreading.

17. The autonomous vehicle of claim 13, wherein each LiDAR point cloud of the plurality of point clouds corresponds to a single 360 degree sweep of the LiDAR system.